AlloPath: A method for identifying protein allosteric pathway based on transfer entropy and hidden Markov model.

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Allostery regulates protein function via communication pathways between sites.
• Identifying these allosteric pathways is crucial but challenging.
• Existing methods often require extensive computation or experimental data.

Purpose of the Study:

• To develop an efficient and accurate computational method for identifying protein allosteric pathways.
• To provide a tool for understanding information transfer in protein regulation.
• To validate the method on well-characterized allosteric proteins.

Main Methods:

• Utilized transfer entropy based on Gaussian Network Model to quantify inter-residue information transfer.
• Incorporated time-delayed correlations to capture dynamic information flow.
• Employed Hidden Markov Model and Viterbi algorithm to identify optimal allosteric pathways.

Main Results:

• Accurately predicted allosteric pathways in hPTP1E PDZ2, Caspase-1, and CheY.
• Identified long-distance pathways (~30 Å) and directional communication routes.
• Demonstrated high reversibility and robustness of the predicted pathways.
• AlloPath relies on static protein structure, avoiding computationally expensive simulations.

Conclusions:

• AlloPath is an effective and computationally inexpensive tool for identifying protein allosteric pathways.
• The method accurately captures functional communication routes in diverse allosteric proteins.
• This approach facilitates the study of protein allostery and regulation.